with regularizer \(\lambda \) .
In the subsequent discussion, we will describe our experimental setup. As mentioned previously, the pitfalls of IRM, including the shortcomings of (IRMv1) in finding predictors with better OOD risks have been studied before [1]}, [2]}. We take an alternate approach where we highlight that even if IRM (IRMv1) works as intended, the underlying data-generating distribution may not allow finding a `good' solution, from both classification risk and fairness considerations. Simultaneously, we will demonstrate how the partial invariance approach can help in this regard.
